Advanced statistical post-processing of ensemble weather forecasts

集合天气预报的高级统计后处理

• 批准号: 1917325
• 金额: --
• 依托单位: UNIVERSITY OF EXETER
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1917325
• 关键词: Advanced; statistical; post; processing; ensemble

* Motivation *Current weather prediction relies on complex numerical models of atmospheric circulation based on the fundamental laws of hydrodynamics and thermodynamics. Forecasts are made by dynamical ensemble prediction systems; to account for the uncertainty in initial conditions an ensemble of initial states is propagated under the model. Despite impressive improvements in the forecast skill of numerical weather prediction in the past decades there are still limitations due to model error and problems in generating ensembles. Model error may be addressed using multi-model ensembles or by stochastic parametrization. Nevertheless, it is observed in the Met Office's practice that forecast ensembles are still biased both in location and dispersion (see also, e.g., Hamill and Colucci, Mon. Wea. Rev., 1997; Gneiting et al., Mon. Wea. Rev., 2005; Raftery et al., Mon. Wea. Rev., 2005). They tend to be underdispersive, leading to overconfident uncertainty estimates and an underestimation of extreme weather events. Systematic biases are significant in subgrid-scale weather phenomena such as UK temperature, precipitation or wind speed at particular locations and state-of-the-art systems occasionally miss extreme weather events within the ensemble distribution. The raw ensemble distribution can thus not be expected to convert directly into a predictive distribution for a variable of interest.* Statistical post-processing *This leads to the idea of combining dynamical and statistical information to improve prediction by statistical post-processing of the dynamical ensemble. Proposed methods range from simple model output statistics schemes known since the 1970s to more advanced approaches such as ensemble dressing (Roulston and Smith, Tellus A, 2003; Wang and Bishop, Q. J. Roy. Meteor. Soc., 2005), Bayesian model averaging (Raftery et al., Mon. Wea. Rev., 2005) and non-homogeneous Gaussian regression (Gneiting et al., Mon. Wea. Rev., 2005). A recent study (Hemri et al., Geophys. Res. Lett., 2014) based on historical ECMWF forecast data has shown that the forecast skill gain over the raw ensemble due to post-processing remains constant even if the skill of the raw ensemble itself increases due to better forecast models and better data assimilation procedures. This indicates that post-processing will add skill for the foreseeable future. Until now, research on statistical post-processing has focussed on the average case, there has been little mention of rare or extreme weather events which are of high socio-economic impact.* Project strategy *The project will tailor existing and develop new methods for statistical post-processing of forecast ensembles with a particular view on extreme weather events. We will develop the promising novel approach of state-dependent post-processing. The post-processing will be conditional on the large-scale circulation regime the forecast model is in. We will use the Met Office's existing catalogue of weather regimes for this purpose. We will use historical data from the Met Office's ensemble prediction system MOGREPS together with the corresponding verifications. We are interested in short- to medium-range weather forecasting where there is considerable variability but still some skill in the ensemble. The research will be conducted in close collaboration with the Met Office as CASE partner. The project has the potential to produce key academic publications as well as real improvements in operational prediction capacity for extreme weather events.* Objectives *The main objectives of the project are:(i) to develop and explore novel methods for statistical post-processing of forecast ensembles for extreme events;(ii) to improve probabilistic prediction of extreme UK temperature, surface pressure, precipitation and wind speed;(iii) to help implement better techniques in the Met Office's operational post-processing suite in order to improve prediction of extreme UK weather events.

* 当前的天气预报依赖于基于流体力学和热力学基本定律的复杂的大气环流数值模型。预报是由动态集合预报系统;考虑到初始条件的不确定性，在模型下传播初始状态的集合。尽管数值天气预报的预报技巧在过去的几十年里有了令人印象深刻的改进，但由于模式误差和生成集合的问题，仍然存在局限性。模型误差可以使用多模型集合或通过随机参数化来解决。然而，在气象局的实践中观察到，预报集合在位置和分散性方面仍然存在偏差（另见，例如，哈米尔和科卢奇，蒙。Wea版本号：1997; Gneiting等人，Mon. Wea版本号：2005; Raftery等人，Mon. Wea.版本号：2005年）。它们往往是分散性不足的，导致过度自信的不确定性估计和极端天气事件的低估。系统偏差在亚网格尺度天气现象中很明显，例如英国特定位置的温度、降水或风速，并且最先进的系统偶尔会错过集合分布中的极端天气事件。因此，不能期望原始总体分布直接转换为感兴趣变量的预测分布。统计后处理 * 这导致了结合动力学和统计信息的想法，通过对动力学系综的统计后处理来改善预测。所提出的方法范围从20世纪70年代以来已知的简单模型输出统计方案到更先进的方法，例如整体着装（Roulston和Smith，Tellus A，2003; Wang和Bishop，Q.罗伊流星索科，2005）、贝叶斯模型平均（Raftery等人，Mon. Wea.版本号：2005）和非齐次高斯回归（Gneiting等人，Mon. Wea版本号：2005年）。最近的一项研究（Hemri等人，地球物理学家。保留信函：2014年）基于历史ECMWF预报数据的研究表明，由于后处理，原始集合的预报技能增益保持不变，即使原始集合本身的技能由于更好的预报模型和更好的数据同化程序而增加。这表明，在可预见的未来，后处理将增加技能。到目前为止，统计后处理的研究主要集中在平均情况下，很少提到具有高度社会经济影响的罕见或极端天气事件。项目战略 * 该项目将调整现有的和开发新的方法，用于对预报集合进行统计后处理，特别是针对极端天气事件。我们将发展有前途的新方法的状态相关的后处理。后处理将取决于预报模式所处的大尺度环流状况。为此，我们将使用气象局现有的天气状况目录。我们将使用气象局集合预报系统MOGREPS的历史数据以及相应的验证。我们感兴趣的是中短期天气预报，其中有相当大的变化，但仍然在合奏一些技巧。这项研究将与作为CASE合作伙伴的英国气象局密切合作。该项目有可能产生重要的学术出版物，并真实的提高极端天气事件的业务预测能力。该项目的主要目标是：（一）开发和探索极端事件预报集合统计后处理的新方法;（二）改进联合王国极端温度、表面压力、降水和风速的概率预测;（三）帮助在气象局的业务后处理套件中实施更好的技术，以改进联合王国极端天气事件的预测。